conceptual understanding as well as practically using them in your deep learning models. We start with sparsity. If your goal was to optimize your brain for storage, you can often trim a lot of useless trivia etc. while retaining the model’s performance? In this chapter we introduce the intuition behind sparsity, different possible methods of picking the connections and nodes to prune, and how to prune a given get you excited yet? Let’s learn about these techniques together! Model Compression Using Sparsity Sparsity or Pruning refers to the technique of removing (pruning) weights during the model training

Semi-supervised Learning (Contd.) Constrained Clustering Distance Metric Learning Manifold based Learning Sparsity based Learning (Compressed Sensing) Feng Li (SDU) Overview September 6, 2023 40 / 57 Constrained

quantization as described in chapter 2. We could also incorporate compression techniques such as sparsity, k-means clustering, etc. which will be discussed in the later chapters. 2. Even after compression